Ophthalmic images are often limited in quality. One key reason is the low level of light that may be safely used for the illumination of the imaged structures of the eye. This results in relatively few photons on the photosensitive detector internal to the imaging device. This limitation is particularly adverse in scanning imaging technologies like Optical Coherence Tomography (OCT) and Scanning Laser Opthalmoscopy (SLO). In this way the signal-to-noise ratio of the produced images is directly limited. In addition, the effective resolution, corresponding to the minimum resolution required to uniquely represent all valid data of the resulting images, is restricted.
When the number of image points (pixels) increases, the number of photons per image point must decrease, adversely affecting the signal-to-noise ratio. The effective resolution is also limited by the exposure time. In static low light scenes, the exposure time may be increased to obtain better signal-to-noise levels. Due to small involuntary eye movements, such a larger exposure time would result in increased blur in ophthalmic images, adversely affecting the quality of the produced images. Therefore the combination of short exposure time and a restricted level of illumination limits the number of pixels. Finally, costs often increase sharply when trying to obtain higher resolution images: in case of a fundus camera more expensive image sensors containing more pixels at higher sensitivity must be used. In case of OCT or SLO a faster, more sensitive and thus more expensive detector must be employed. Further, the scan rate must be increased by using a faster scanner, however, since OCT and SLO systems usually employ scanners that already operate close to their mechanical limits, increasing their speed may be very expensive or impossible.
The reduction of noise in single images has previously been achieved by various filtering techniques. These approaches work by statistical analysis of surrounding pixels. As these methods work on single images, they do not consider neighboring pixels in image series.
Previous work reduced noise in image series by averaging multiple registered images. One straight-forward implementation is to calculate a pixel-wise mean value. Other, more robust statistics have also been applied, such as the median filter, which may be applied both spatially and temporally.
To increase the resolution of single images, interpolation is commonly used. In linear interpolation, a new pixel is added in between two existing pixels by calculating the average value of the two real pixels. Other interpolation methods (e.g., cubic, spline) apply more advanced calculations that may involve an increasing number of neighboring pixels. Interpolation may be used to correct for distortions or anisotropy in the images, or to obtain larger images without stair-case effects (resulting in jagged lines). However, they cannot introduce valid information in higher spatial frequency bands and will therefore result in blurred images and may introduce artifacts. Therefore, while the number of pixels in the image is increased, the effective resolution is unaltered.
Previous work to increase the effective resolution employed mosaicking, which combines multiple smaller images into one high resolution image. This requires the acquisition of a large number of different images, each showing a detail of the full imaged area. These images are then stitched together to produce the high resolution image. Therefore, the inter-image movement needs to be large (in the order of the size of the images themselves), while the intra-image movement should be small (to prevent blurring). This process is thus time consuming and the controlling the movement at such a high precision may be very difficult.
Super-resolution is the name of a family of methods to combine several largely overlapping low-quality images into one or more high-quality images. These high-quality images show less noise, may be corrected for geometric distortions and have a higher effective resolution. One prerequisite for super-resolution is a known displacement between the low-quality images in the series. In addition, the low-quality or low-resolution images should be aliased, which means that, due to a sampling frequency smaller than the Nyquist frequency, high frequency bands are wrapped to low frequencies.
An example of actual application of super-resolution is in infra-red imaging in military settings, where a low resolution detector is placed on a vibrating stage and the resulting low resolution image series are post-processed with super-resolution algorithms in order to generate a higher resolution image.
Prior art uses of super-resolution algorithms require that the camera be moved with some known amount, for example by a motorized stage. In ophthalmic imaging, this would require fixation of the eye, which is very unpleasant for the patient.